Roundup 10/18/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours.

Note to readers: Big Analytics is now ML/DL.

Good Reads

Two widely read items from The Boston Consulting Group:

— Nikolas Lang et. al deliver a 10-part white paper on the impact of autonomous vehicles.

— Christine Barton et. al. explain why companies can’t turn customer insights into growth.


— Finnish company Tieto appoints a chatbot to its leadership team. No, I’m not kidding.

— Gartner’s Peter Sondergaard says that AI and ML are at the heart of digital transformation.

— Barry Schwartz interviews Google Search exec Gary Illyes, who says search marketers should keep their eyes on machine learning, Accelerated Mobile Pages (AMP), and structured data.

— Tim Cook describes Apple’s plans to use AI: increasing battery life; improving Apple Music recommendations; remembering where you park your car. Apple needs to get right on the music recommendations because right now they suck.


— The Royal Navy isn’t worried about naval robot drones. And they thought the Prince of Wales was unsinkable.

— Emmanuelle Rieuf reviews Cathy O’Neil’s Weapons of Math Destruction. So does Jo Craven McGinty in the Wall Street Journal.


— In Dataconomy, Per Harald Borgen describes his experience learning machine learning in a year.

— Google releases a new version of its Course Builder software for online training.


— Scientists at Harvard teach AI how to ace an organic chemistry test. It doesn’t simply memorize the answers, but can solve problems it hasn’t seen before.


— Apple hires Carnegie Mellon University professor Ruslan Salakhutdinov as Director of AI research. Linkapalooza here.


— Serdar Yegualp summarizes four Google data sets for machine learning:

  • The Open Images Dataset
  • YouTube – 8M Dataset
  • Google Books Ngrams
  • Google Trends Datastore

He also notes the Google Public Data Directory, which offers a portal to data sets from providers around the world.

Methods and Techniques

— Ariful Islam Mondal explains response modeling with machine learning in R.

— François Maillet of explains how to use MLDB for machine learning. MLDB looks like an exciting project.


— Nick Ismail explains how companies can use machine learning to build B2B relationships.


— Andrew Oliver proposes dropping seven technologies from the Big Data ecosystem: MapReduce, Storm, Pig, Java, Tez, Oozie, and Flume. He forgets to mention Mahout, which is forgivable since nobody uses it.

— datango, a division of PARIS AG, launches a business process support bot.

— SYSTRAN announces beta availability of a new translation engine branded as Pure Neural Machine Translation (PNMT.) The system offers real-time translation in more than thirty languages.

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